Compensations for an Assistive Robotic Interface

Abstract

The goal of assistive robotic devices, such as a wheelchair-mounted robotic arms (WMRA), is to increase users’ functional independence. At odds with this goal is the fact that device interfaces tend to be rigid, requiring the user to adapt, rather than adapting to the user. Paperno, et al. (2016) identified key physical, cognitive, and sensory capabilities that affect an individual’s performance of simulated activities of daily living (e.g. picking up an object from the floor) while using a WMRA. Greater visual abilities (visual acuity, contrast sensitivity, and depth perception), cognitive abilities (processing speed, working memory, and spatial ability) and physical abilities (dexterity) resulted in participants completing tasks more quickly and with fewer total moves. We propose that interfaces should adapt to compensate for deficits in these capabilities to support a wider range of users. A variety of compensations should be developed and tested in order to identify the most effective techniques. For instance, object segmentation, a computer vision technique that separates objects and background in a visual scene, may compensate for deficits in contrast sensitivity, depth perception, processing speed, and working memories. However, contrast sensitivity may be better compensated for by use of a simple yellow filter on the screen, mimicking yellow lenses in glasses used for the same purpose. Similarly, depth perception limitations may be better overcome through the use of multiple camera views or by automating the pick-up and release mechanisms of the gripper. Thus there may be one compensation that facilitates WMRA use for a multitude of decrements or each factor may be better served by a specific separate compensation. In incorporating the effective compensations into the interface software, there should also be a capability of identifying which specific compensations should be activated for an individual user. For this we propose testing for these important individual differences should be included within the software. Virtual or online testing already exist for many of the identified factors and can be modified to fit our purpose. This is especially the case if gamification principles are applied as testing will engage user interest. In this way, the software can adjust compensations as a user’s visual, cognitive, and physical abilities change over time. Future research ventures will include identifying the most beneficial compensation for each identified individual difference and developing virtual gamified measures for those individual differences.